Okey, I understood...
I have a matrix of 40 rows (samples) and 29 columns (species). In the
ordination graph the data divide in two clades ( as i supposed they must
to)... and that's my best solution for reduce the Stress...
metaMDS(sqrtABCD, distance = bray, k = 23, trymax = 50, autotransform
On 2/12/09 19:55 PM, Gian Maria Niccolò Benucci gian.benu...@gmail.com
wrote:
Okey, I understood...
I have a matrix of 40 rows (samples) and 29 columns (species). In the
ordination graph the data divide in two clades ( as i supposed they must
to)... and that's my best solution for reduce the
On 2/12/09 19:55 PM, Gian Maria Niccolò Benucci gian.benu...@gmail.com
wrote:
... I supposed, that If we use as many dimensions as there are variables,
then we can perfectly reproduce the observed distance matrix. Isn't it?
Gian, Not quite so. I think it would be useful to consult a good book,
Jari,
Yes, you right, I am sorry I didn't say that I was talking about Euclidean
measure of distance in that passage, I know that other distance are
different, and finally I understood the non-linear regression stuff, now is
much more clear!
If you have some good book titles I would appreciate a
